Word Sense Language Model for Information Retrieval

2006 ◽  
pp. 158-171
Author(s):  
Liqi Gao ◽  
Yu Zhang ◽  
Ting Liu ◽  
Guiping Liu
Keyword(s):  
Information Retrieval ◽  
Language Model ◽  
Word Sense

Developing the Persian Wordnet of Verbs Using Supervised Learning

2021 ◽  
Vol 20 (4) ◽  
pp. 1-18
Author(s):  
Zahra Mousavi ◽  
Heshaam Faili
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Language Processing ◽  
Supervised Classification ◽  
Word Sense ◽  
Direct Influence ◽  
Training Set ◽  
Bilingual Dictionary ◽  
Automated Method ◽  
Princeton Wordnet

Nowadays, wordnets are extensively used as a major resource in natural language processing and information retrieval tasks. Therefore, the accuracy of wordnets has a direct influence on the performance of the involved applications. This paper presents a fully-automated method for extending a previously developed Persian wordnet to cover more comprehensive and accurate verbal entries. At first, by using a bilingual dictionary, some Persian verbs are linked to Princeton WordNet synsets. A feature set related to the semantic behavior of compound verbs as the majority of Persian verbs is proposed. This feature set is employed in a supervised classification system to select the proper links for inclusion in the wordnet. We also benefit from a pre-existing Persian wordnet, FarsNet, and a similarity-based method to produce a training set. This is the largest automatically developed Persian wordnet with more than 27,000 words, 28,000 PWN synsets and 67,000 word-sense pairs that substantially outperforms the previous Persian wordnet with about 16,000 words, 22,000 PWN synsets and 38,000 word-sense pairs.

scholarly journals Linear Algebraic Structure of Word Senses, with Applications to Polysemy

2018 ◽  
Vol 6 ◽  
pp. 483-495 ◽  
Author(s):  
Sanjeev Arora ◽  
Yuanzhi Li ◽  
Yingyu Liang ◽  
Tengyu Ma ◽  
Andrej Risteski
Keyword(s):  
Information Retrieval ◽  
Random Walk ◽  
Algebraic Structure ◽  
Sparse Coding ◽  
Word Sense ◽  
Word Embeddings ◽  
Linear Superposition ◽  
Empirical Tests ◽  
Embedding Methods ◽  
Word Senses

Word embeddings are ubiquitous in NLP and information retrieval, but it is unclear what they represent when the word is polysemous. Here it is shown that multiple word senses reside in linear superposition within the word embedding and simple sparse coding can recover vectors that approximately capture the senses. The success of our approach, which applies to several embedding methods, is mathematically explained using a variant of the random walk on discourses model (Arora et al., 2016). A novel aspect of our technique is that each extracted word sense is accompanied by one of about 2000 “discourse atoms” that gives a succinct description of which other words co-occur with that word sense. Discourse atoms can be of independent interest, and make the method potentially more useful. Empirical tests are used to verify and support the theory.

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